






'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class   lowerCAmelCase__ ( _lowerCAmelCase						,_lowerCAmelCase						,unittest.TestCase  ):
			A     				= IFInpaintingPipeline
			A     				= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			A     				= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A     				= PipelineTesterMixin.required_optional_params - {'''latents'''}

			def 						__UpperCamelCase				(					self					:		Optional[int]	) ->  Optional[int]:







				"""simple docstring"""






				return self._get_dummy_components()

			def 						__UpperCamelCase				(					self					:		Optional[Any]     ,		UpperCamelCase_					:		Optional[Any]     ,		UpperCamelCase_					:		List[Any]=0	) ->  Optional[Any]:







				"""simple docstring"""






				if str(UpperCamelCase_	).startswith('''mps'''	):
					lowerCamelCase_					:  Optional[int]						=    torch.manual_seed(UpperCamelCase_	)
				else:
					lowerCamelCase_					:  Optional[int]						=    torch.Generator(device=UpperCamelCase_	).manual_seed(UpperCamelCase_	)

				lowerCamelCase_					:  Optional[int]						=    floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(UpperCamelCase_	)	).to(UpperCamelCase_	)
				lowerCamelCase_					:  Tuple						=    floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(UpperCamelCase_	)	).to(UpperCamelCase_	)

				lowerCamelCase_					:  Dict						=    {
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''image''': image,
				    '''mask_image''': mask_image,
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''output_type''': '''numpy''',
				}

				return inputs

			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()     ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,		)
			def 						__UpperCamelCase				(					self					:		Union[str, Any]	) ->  Union[str, Any]:







				"""simple docstring"""






				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)

			def 						__UpperCamelCase				(					self					:		Tuple	) ->  Optional[Any]:







				"""simple docstring"""






				self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != '''cuda'''     ,		reason='''float16 requires CUDA'''	)
			def 						__UpperCamelCase				(					self					:		str	) ->  Union[str, Any]:







				"""simple docstring"""






				super().test_save_load_floataa(expected_max_diff=1e-1	)

			def 						__UpperCamelCase				(					self					:		int	) ->  int:







				"""simple docstring"""






				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)

			def 						__UpperCamelCase				(					self					:		Tuple	) ->  Optional[Any]:







				"""simple docstring"""






				self._test_save_load_local()





			def 						__UpperCamelCase				(					self					:		Optional[Any]	) ->  Dict:







				"""simple docstring"""






				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2     ,		)












'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


